Early-stage multi-cancer detection using an extracellular vesicle protein-based blood test

Abstract

Background: Detecting cancer at early stages significantly increases patient survival rates. Because lethal solid tumors often produce few symptoms before progressing to advanced, metastatic disease, diagnosis frequently occurs when surgical resection is no longer curative. One promising approach to detect early-stage, curable cancers uses biomarkers present in circulating extracellular vesicles (EVs). To explore the feasibility of this approach, we developed an EV-based blood biomarker classifier from EV protein profiles to detect stages I and II pancreatic, ovarian, and bladder cancer.

Methods: Utilizing an alternating current electrokinetics (ACE) platform to purify EVs from plasma, we use multi-marker EV-protein measurements to develop a machine learning algorithm that can discriminate cancer cases from controls. The ACE isolation method requires small sample volumes, and the streamlined process permits integration into high-throughput workflows.

Results: In this case-control pilot study, comparison of 139 pathologically confirmed stage I and II cancer cases representing pancreatic, ovarian, or bladder patients against 184 control subjects yields an area under the curve (AUC) of 0.95 (95% CI: 0.92 to 0.97), with sensitivity of 71.2% (95% CI: 63.2 to 78.1) at 99.5% (97.0 to 99.9) specificity. Sensitivity is similar at both early stages [stage I: 70.5% (60.2 to 79.0) and stage II: 72.5% (59.1 to 82.9)]. Detection of stage I cancer reaches 95.5% in pancreatic, 74.4% in ovarian (73.1% in Stage IA) and 43.8% in bladder cancer.

Conclusions: This work demonstrates that an EV-based, multi-cancer test has potential clinical value for early cancer detection and warrants future expanded studies involving prospective cohorts with multi-year follow-up.

Keywords: Bladder cancer; Cancer screening; Ovarian cancer; Pancreatic cancer.

Conflict of interest statement

Competing interestsR. Krishnan, J.P.H., R.T., I.C., H.I.B., V.O., J.M.L., O.P., L.A., J.R.H., G.S., and D.S. are employees of Biological Dynamics. R. Krishnan is a co-founder and board member of Biological Dynamics. R. Krishnan is an inventor on patents held by the University of California San Diego and Biological Dynamics that covers aspects of the Verita™ platform used in this manuscript. The terms of these arrangements are being managed by the University of California–San Diego in accordance with its conflict-of-interest policies. R. Kurzrock receives research funding from Boehringer Ingelheim, Debiopharm, Foundation Medicine, Genentech, Grifols, Guardant, Incyte, Konica Minolta, Medimmune, Merck Serono, Omniseq, Pfizer, Sequenom, Takeda, and TopAlliance; as well as consultant and/or speaker fees and/or advisory board for Actuate Therapeutics, Bicara Therapeutics, Inc., Biological Dynamics, Neomed, Pfizer, Roche, TD2/Volastra, Turning Point Therapeutics, X-Biotech; has an equity interest in CureMatch Inc. and ID by DNA; serves on the Board of CureMatch and CureMetrix, and is a co-founder of CureMatch. R.E. receives research funding to his institution from Clovis Oncology, AVITA, Merck and AstraZenca, as well as consultant and/or speaker fees and/or advisory board from AstraZeneca, GSK/Tesaro, Seagen, Myriad, Merck, Eisai as well as the GOG Foundation. A.K. consultant/advisory board member for Abbott Molecular, Arquer, ArTara, Asieris, Astra Zeneca, BioClin Therapeutics, Biological Dynamics, BMS, Cepheid, Cold Genesys, Eisai, Engene, Inc., Ferring, FerGene, Imagin, Janssen, MDxHealth, Medac, Merck, Pfizer, Photocure, ProTara, Roviant, Seattle Genetics, Sessen Bio, Theralase, TMC Innovation, US Biotest. AM Kamat has received grant/research support from Adolor, BMS, FKD Industries, Heat Biologics, Merck, Photocure, SWOG/NIH, SPORE, AIBCCR. A.M.K. has patents for CyPRIT (Cytokine Predictors of Response to Intravesical Therapy) jointly with UT MD Anderson Cancer Center is a paid consultant of Biological Dynamics. S.M.L. is a co-founder of io9. N.J.S., S.M.L., P.B., and M.A. are members of the Biological Dynamics scientific advisory board. S.M.L. received principal investigator support from the UC San Diego Moores Cancer Center, Specialized Cancer Center Support Grant NIH/NCI P30CA023100, and SU2C-AACR-DT-25-17 Pancreatic Cancer Interception Dream Team award. A.M.L. and R.E. declare no competing interests. P.B. holds equity in CytoBay, Synergenz, and LungLifeAI, all cancer diagnostic or risk assessment enterprises.

© The Author(s) 2022.

Figures

Fig. 1. Schematic showing EV isolation workflows using either Verita™ or ultracentrifugation methods.

a Workflow using the Verita™ Isolation platform. As plasma samples are flowed onto the energized AC Electrokinetics (ACE) microelectrode array, EVs are collected onto the electrodes. Unbound materials are removed with a buffer wash, the electric field turned off, and EVs are eluted into the buffer. b Workflow for differential ultracentrifugation. Plasma samples are diluted, and large debris pelleted by a low-speed centrifugation step. Supernatants are removed and subjected to two additional cycles of low-speed centrifugation. EVs in the cleared supernatants are then ultracentrifuged two times, and lastly the pellet is resuspended in buffer.

Fig. 2. Development of classification algorithm for multi-cancer early detection.

Biomarker selection is performed via recursive feature elimination (RFE) with cross validation. After the biomarkers are selected, the dataset is split into training and test sets. The training set is used for the determination of the coefficients in the logistic regression for each biomarker and the test set is used to evaluate the performance of the logistic regression fit from the training set in a held-out test set. Finally, the process of splitting the dataset into training and test sets is randomly repeated 100 times for performance confirmation.

Fig. 3. Characterization of EVs isolated by either Verita™ or differential ultracentrifugation.

a Distribution of particle sizes as determined by nanoparticle tracking analysis. Blue line represents the particle distribution from Verita™ isolation (N = 25 subjects) while the gray line represents the isolation from differential ultracentrifugation (N = 25 subjects). b Levels of residual contaminating total proteins based on Qubit™ protein assay (N = 25 subjects for each isolation methodology). The ability to differentiate cancer cases from controls based on biomarkers CA 19-9 and CA125 is shown for EVs isolated using the Verita™ isolation in panel (c), and EVs isolated by differential ultracentrifugation in panel (d). In both (c), (d) panels, the N for Controls is 11 subjects, and the N for Cancers is 14 subjects.

Fig. 4. Overall performance for detecting the presence of early cancer using an EV protein-based logistic classifier.

a ROC curves from comparison of the cancer cases (N = 139) to the controls (N = 184) on the held-out test sets; black line represents the averaged curve of 100 independently resampled held-out test sets (gray lines). AUC area under the ROC curve. b Sensitivities at >99% specificity for detecting either stage I or stage II pancreatic, ovarian, and bladder cancers combined. N for stage I cancers is 88 subjects and the N for stage II cancers is 51 subjects. c Sensitivity at >99% specificity for detecting either stages I and II pancreatic (N = 47), ovarian (N = 44), or bladder (N = 48) cancer. Error bars in both panels (b), (c) represent the two-sided 95% Wilson confidence intervals.

Fig. 5. Sensitivities by stage for simultaneous detection of three cancer types using EV protein biomarkers.

a Sensitivity for detecting either stage I (N = 22) or stage II (N = 25) pancreatic cancer. b Sensitivity for detecting either stage I (N = 39) or stage II (N = 5) ovarian cancer. c Sensitivity for detecting either stage I (N = 27) or stage II (N = 21) bladder cancer. All sensitivities represent values at >99% specificity for the held-out test sets. Error bars in all panels represent the two-sided 95% Wilson confidence intervals.